Forest edges negatively influence daily nest survival rates of a grassland Tinamou, the Spotted Nothura (<i>Nothura maculosa</i>)
Bibliographic record
Abstract
Grassland degradation and fragmentation produced by land use have globally impacted biodiversity. In the Neotropics, the Pampas Grasslands have been greatly altered by agriculture and the introduction of exotic trees. To evaluate the effects of changing habitat features on indigenous grassland fauna, we studied a breeding population of a ground-nesting bird, the Spotted Nothura (Nothura maculosa (Temminck, 1815)), in a natural grassland under cattle grazing in central-east Argentina. We estimated daily nest survival rate (DSR) and modeled it as a function of habitat (distance to habitat edges, cattle density, and nest concealment) and temporal factors. Of the 80 nests found, 64 (80%) failed, predation being the principal cause of failure. DSR was 0.874, estimating a cumulative survival of only 6.8% throughout egg laying and incubation. DSR increased with distance to continuous forests and decreased with nest age. Nests located near forest edges could have increased predation risk because they are potentially exposed to forest-dwelling predators in addition to grassland-dependent predators. Considering the low success found and the ongoing invasion of exotic trees in the region, we encourage governments to protect large areas of grassland to ensure adequate nest success for Tinamous and other ground-nesting birds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".